Guangdong experienced the largest dengue epidemic in recent history. In 2014, the number of dengue cases was the highest in the previous 10 years and comprised more than 90% of all cases. In order to analyze heterogeneous transmission of dengue, a multivariate time series model decomposing dengue risk additively into endemic, autoregressive and spatiotemporal components was used to model dengue transmission. Moreover, random effects were introduced in the model to deal with heterogeneous dengue transmission and incidence levels and power law approach was embedded into the model to account for spatial interaction. There was little spatial variation in the autoregressive component. In contrast, for the endemic component, there was a pronounced heterogeneity between the Pearl River Delta area and the remaining districts. For the spatiotemporal component, there was considerable heterogeneity across districts with highest values in some western and eastern department. The results showed that the patterns driving dengue transmission were found by using clustering analysis. And endemic component contribution seems to be important in the Pearl River Delta area, where the incidence is high (95 per 100,000), while areas with relatively low incidence (4 per 100,000) are highly dependent on spatiotemporal spread and local autoregression.

f1: Dengue map at the district level in Guangdong, China, 2014 (created with ArcGis Professional software version 10.2, http://www.esri.com/).

Mentions:
A total of 45,171 dengue cases were reported from 21 districts of Guangdong Province in 2014. The provincial capital, Guangzhou, has 37,394 cases (82.8% of all cases), followed by Foshan (7.8%) and Zhongshan (1.5%) (see Fig. 1). The incidence varies regionally with the highest incidence concentrated in the central districts of Guangdong (see Fig. 1). The majority of cases (42,538, 94.2%) were reported in September and October (weeks 35 to 44). The number of cases peaked in the week (9698 cases, about 21.5% of all cases), then decreased towards the end of the year, which indicates a clear seasonal pattern. In addition, the cumulative number of cases grows, similarly to a logistic growth curve (Fig. 2).

f1: Dengue map at the district level in Guangdong, China, 2014 (created with ArcGis Professional software version 10.2, http://www.esri.com/).

Mentions:
A total of 45,171 dengue cases were reported from 21 districts of Guangdong Province in 2014. The provincial capital, Guangzhou, has 37,394 cases (82.8% of all cases), followed by Foshan (7.8%) and Zhongshan (1.5%) (see Fig. 1). The incidence varies regionally with the highest incidence concentrated in the central districts of Guangdong (see Fig. 1). The majority of cases (42,538, 94.2%) were reported in September and October (weeks 35 to 44). The number of cases peaked in the week (9698 cases, about 21.5% of all cases), then decreased towards the end of the year, which indicates a clear seasonal pattern. In addition, the cumulative number of cases grows, similarly to a logistic growth curve (Fig. 2).

Guangdong experienced the largest dengue epidemic in recent history. In 2014, the number of dengue cases was the highest in the previous 10 years and comprised more than 90% of all cases. In order to analyze heterogeneous transmission of dengue, a multivariate time series model decomposing dengue risk additively into endemic, autoregressive and spatiotemporal components was used to model dengue transmission. Moreover, random effects were introduced in the model to deal with heterogeneous dengue transmission and incidence levels and power law approach was embedded into the model to account for spatial interaction. There was little spatial variation in the autoregressive component. In contrast, for the endemic component, there was a pronounced heterogeneity between the Pearl River Delta area and the remaining districts. For the spatiotemporal component, there was considerable heterogeneity across districts with highest values in some western and eastern department. The results showed that the patterns driving dengue transmission were found by using clustering analysis. And endemic component contribution seems to be important in the Pearl River Delta area, where the incidence is high (95 per 100,000), while areas with relatively low incidence (4 per 100,000) are highly dependent on spatiotemporal spread and local autoregression.